FedNet2Net: Saving Communication and Computations in Federated Learning with Model Growing. (arXiv:2207.09568v1 [cs.LG])

Federated learning (FL) is a recently developed area of machine learning, in
which the private data of a large number of distributed clients is used to
develop a global model under the coordination of a central server without
explicitly exposing the data. The standard FL strategy has a number of
significant bottlenecks including large communication requirements and high
impact on the clients' resources. Several strategies have been described in the
literature trying to address these issues. In this paper, a …

communication federated learning lg